Croatian lijep, Serbian lep) which was already encoded during the Croatian annotation.

after each round of annotation, the list of OOVs was regenerated, and the paradigm prediction model was retrained 1 http://sourceforge.net/p/apertium/svn/ HEaD/tree/languages/apertium-hbs/ 2 HBS is the ISO 639-3 code for the macrolanguage covering the three languages in question lemmas surface forms hrLex 99,680 4,971,257 srLex 105,358 5,327,361  even after finishing all the annotation rounds and greatly expanding the apertium lexicon, its format is still not ideal for our purposes.


Corpora are, additionally, cheaper resources to produce.

adding the large lexicons does push the results significantly further, a phenomenon much less observable with the HunPos tagger.


In total there are 163 contractions in TGermaCorp, which are summarized in Table 1.

since we do not assume that concatenated POS constitute proper parts of speech, we bifurcate them and use the split, "atomic" categories for analyses.


Our assessment shows that in terms of lexical similarity of sentences and their complexity the TGermaCorp and the Tiger treebank are comparable.

part of the diversity is due to the influence of proper names, which occur with different frequencies in various resources.


As the results presented show, models with Senna initialization outperformed those initialized with GloVe in almost all experimental setups; slightly on the POS and in a larger extent on the NER dataset.

since these vectors were trained on different datasets, we cannot conclude that the model that generated Senna is the better suited to this task, we only compare the utility of the resulting word vectors.


Religious text such as the Holy Qur'an are fully diacritized.

in newswire text, 1.6% of all words have at least one diacritic indicated by their author, mostly to disambiguate the text for readers (Habash, 2010).


This is why the partial diacritization system performs better than the baseline even with a 0% diacritization rate (0.3% absolute increase on Star).

applying the partial diacritization system on a raw text performs worse than the baseline (0.4% absolute decrease on Star).


Similar to two-part conjunctions there are also pairs of clitics.

the clitics that can make up the first part of a pair can also occur on their own.


Data needed for training dedicated tools for these texts is often not available.

in case the non-canonical text is related to another language, e.g., the modern stage of the language, we can exploit this relatedness to facilitate tagging.


This is crucial to know since this means that neither tools developed for MHG (Dipper, 2010;Bollmann, 2013) nor for standard German will work reliably.

its relative closeness to both can nevertheless be beneficial.


Model transfer from MHG and stacking outperform all approaches without external resources with accuracy scores of 0.73 and 0.77, respectively.

the LSTM can outperform two of the extended approaches, namely model transfer from NHG and tritraining.


There is related work on the automatic discovery of concept signatures, described by overlapping (Lin and Pantel, 2002) or fuzzy (Velldal, 2005) word clusters.

although words in the same cluster have a strong relation, they are not exclusively synonyms, and thus a cluster cannot be seen as a wordnet synset.


Those numbers can be compared with TeP's, handcrafted, and thus a possible reference.

the average number of senses and synset size in CLIP 2.0 are closer to TeP's.


Such query would, however, have been far less accurate since on the one hand, in the case of lyrics it would have included non-lyrical text within volumes of poems, such as prose (prefaces, introductions, explanatory texts, notes), indices, title pages etc.

poems contained by works of prose would have been evaluated as prose.


This pipeline architecture is appealing for various reasons, including modularity, modeling convenience, and manageable computational complexity.

it suffers from the error propagation problem: errors made in one sub-task are propagated to the next sub-task in the sequence.


In particular, when applying an MLN to different problem instances, the first-order formulas remain unchanged: all we need to change are the evidence predicates (i.e., the observations).

5 an ILP program cannot encode a problem instance compactly.


Then, they asked a single member in their research group to rate the credibility of those 1000 articles on a 5 Likert scale.

this corpus is in English, and was rated by a single person only, which makes it inapplicable in our case.


For example, judging the credibility of a tweet using only its text is not enough; instead one may additionally rely on the author background, expertise and external web references.

a blog post might contain enough cues in its text to assess its credibility.


For example, consider Figure 1, which shows random input vs. input selected using AL to consider the network structure.

this brings us to what we define as the chickenand-egg corpus and model conundrum, which refers to how AL often happens in a closed-loop process, the underlying model or models directly influencing which data is selected for annotation, which improves the model's accuracy, and so on.


the task of active learning is then to find a set of points 1 While in practice it may be sufficient to optimise a given model, and therefore mould a corpus using AL to fit it, this loses sight of the bigger aim, that of studying phenomena for understanding how something works; further, it has been shown to even be detrimental in some cases to apply data acquired for one model using AL to another model (Baldridge, 2004;Rubens and Sugiyama, 2006;Sugiyama and Rubens, 2008 to label X L as to minimize the expected prediction error: in our settings the task is unknown, i.e.


Predominately, the purpose of the subgraph-based characteristics should be to ensure properties of the whole graph are preserved well witin the subgraph.

note that the subgraph does not need to precisely reflect the characteristics of the overall graph (this is a goal of network sampling introduced in Section 3.3.


Given a relatively regular structure of reported speechalthough quite different in different languages, and even across varieties, see (Santos, 1998) for some discussion -, rule-based approaches to QE are often extremely successful.

purely formal marks that indicate the presence of a quotation, such as quotes in English, are not unique to this purpose, hence recognizing the specific verbs that are used in these contexts is highly relevant.


This QM tries to find out whether the Agent uses positive words during the conversation with the customer.

since agents do not use very negative (rude words for example) or very positive words, the association of words with sentiment labels is highly subjective.


Thus, for instance, to find sentences in a topic area, one must join a clutch of tables together (posts, discussions, topics, texts, and sentences).

this is balanced by the fact that we do not have to loop over the entire dataset to find objects of interest.


Corpora such as the HCRC MapTask corpus of dyadic information gap task-based conversations (Anderson et al., 1991), ICSI and AMI multiparty meeting corpora (Janin et al., 2003;McCowan et al., 2005), and resources such as recordings of televised political interviews (Beattie, 1983) have contributed greatly to our understanding of different facets of spoken interaction such as timing, turntaking, and dialogue architecture.

the speech in these resources, while spontaneous and conversational, cannot be considered casual talk, and the results obtained from their analysis may not transfer to the less studied 'unmarked' case of casual conversation.


Therefore, it may seem that the processing for the purposes of statistics and research (including, arguably, the improvement of the translation model) may be allowed.

according to WP29's opinion one of the key factors in assessing purpose compatibility should be 'the context in which the data have been collected and the reasonable expectations of the data subjects as to their further use'.


Since mobile devices have feature-rich configurations and provide diverse functions, the use of mobile devices combined with the language resources of cloud environments is high promising for achieving a wide range communication that goes beyond the current language barrier.

there are mismatches between using resources of mobile devices and services in the cloud such as the different communication protocol and different input and output methods.


In addition, critical issues of composing, and integrating different types of services need to be solved.

these tasks are not easy for all developers.


The speech recognition service, translation service and text-to-speech service are required.

both mobile device and the cloud provide the speech recognition service and text-to-speech service.


Unfortunately, many tools and formats were created for just a single kind of annotation, for instance the Tiger-XML format (Lezius et al., 2002), which was created exclusively for constituents, or the MMAX2 format (Müller and Strube, 2006), which was created exclusively for coreferences.

in order to build multi-layer corpora we need to combine different kinds of annotation.


Thus, the maximum size of corpora it can process mainly depends on the number of nodes and edges in a corpus, not on token figures.

since ANNIS uses the PostgreSQL relational database 12 it is key to have a powerful server with sufficient main memory when querying corpora containing more than 1m nodes.


Dutch and Swedish data will not be used to create corpora because they are represented too sparsely in the original data sets.

based on the previous experience with similar crawl data, the English COCO1507 corpus is expected to be 110 billion tokens large.


By the time of this writing, they have been implemented and tested on small data sets but not on the actual large data sets.

both the COCOA data files and the Python tools will be made available still in 2016. s per file download WARC and COCOA 60s COCOA/WARC merger 20s total 80s Table 2: Estimated overhead for end users involved in the corpus reconstruction process on a single Intel Core i7 CPU with a 100 Mbit/s downstream.


This feature allows multiple users that have access on the same document to interact with each other (create, edit or delete annotations) and view the changes at the same time.

to this approach, annotation solutions such as GATE Teamware and WebAnno create a separate view of the annotated document for each annotator and only the curator of the annotation task is able to review the results of the annotation process.


For instance, within the META-SHARE project, the META-SHARE Commons set of licenses was produced to allow META-SHARE members and Extraneous Depositors to make their resources available to other META-SHARE network members only.

the abandon of these licenses is now highly recommended, due to the fact that they are now well and fully covered within the latest Creative Commons version 4.0.


LRE does not explicitly seek to work on low resource languages.

since LRE's goal is to develop robust technologies that perform well even as the number of linguistic varieties increases, and since the number of well-resourced varieties is relatively small, it is inevitable that LRE would include low resource varieties.


If the language has too few resources, the project could mire in LR creation.

if the language were too well resourced, the experience might not represent other low resource languages.


This type of content is more stable, since it is meant to stay unmodified overtime.

corporate-generated content also includes more dynamic textual types, such as press releases, news, event announcements, calendars and even blog entries.


Another strategy is to refine the SMT system by including a pre-processing step in which potential spelling errors are modelled (through a Confusion Network) and subsequently recovered by the decoder on the basis of a character n-gram language model (Bertoldi et al., 2010).


As a matter of fact, for an e-commerce website to be able to replicate this approach in a realistic situation, it would need to have a huge amount of translated reviews already available to train on (which would be applicable only to a handful of very big e-commerce players).

by testing on a different dataset, our approach is more robust and its applicability open to any e-commerce player, e.g.


To meet the demand, several benchmarks have been constructed for English TOEFL , BLESS (Baroni and Lenci, 2011), LENCI/Benotto  and EVALution 1.0 .

to date there is no dataset especially designed for DSMs in Mandarin, Chinese.


The presence of a prefix (pro-) in prokuhao indicates that the described event is completed; i.e., verb aspect is encoded via a lexical derivation.

this encoding is not direct, as similar verb affixes are not formal aspect markers.


We come back to the the cases where such forms are not marginal later.

changing grammatical aspect from perfective to imperfective without a prefix is realised through various changes in the verb stem.


pisatinapisati -*napisivati 'write',čistiti -očistiti -*očišćavati 'clean', citatipročitati -*pročitavati 'read').

there is no obvious regularity as to which prefixes behave in this way.


pisnuti 'peep' is not derived from pisati 'write') In these cases, relatedness between derivations cannot be established based on a simple set of rules.

the fact that related forms tend to be morphologically similar and carry similar meanings constitutes a sound basis for various stochastic approaches involving machine learning.


If there is a shared synset node between these two paths and this node distance is short, the word in the sentence is regarded as 'Literal'.

it is possible to be a metonymic expression if there is no shared synset or the node distance is long.


It would be preferable to encode only the fact that Mary broke a vase.

because the relation CAPABLE-OF cannot account for the semantics of the verb "break", it generates an underspecified relation between Mary and the vase.


Attempts to address these limitations was tackled in our previous work (Goodwin and Harabagiu, 2013) where a qualified medical knowledge graph (QMKG) was presented.

the QMKG does not capture compositional semantics of medical concepts, nor the lexical relations between them.


This allowed us to leverage Spark's GraphX library (Xin et al., 2013) for graph-parallel computations.

processing such a large knowledge graph is challenging, even in a distributed architecture.


We implemented this by a map-reduce counting operation.

this did not account for another problem which arose during knowledge acquisition: knowledge sparsity.


Based on the assumption that related or similar class labels tend to co-occur in documents more often than unrelated labels , we expect that vector representations for similar labels will tend to be closer to each other due to the associations via documents for which such labels co-occur.

relationships of this sort could conceivably also be discoverable simply by counting co-occurrences of class labels.


Indeed, our experimental results in Section 5 show that this turns out to already be a strong baseline.

this idea only works for labels that have been observed together.


We conclude the paper in Section 9.. Our resource is publicly released at: github.com/CorentinDumont/QA_Minecraft  Minecraft (Figure 1) is a sandbox video game, which means that the player is free to choose the actions he wants to execute, and the order of these actions.

as all video games, the number of possible actions is limited.


Tireless efforts of data collection of speech data and their transcriptions from the early stage of NLP and SLP researches drastically improved accuracies of a variety of NLP and SLP tasks.

we still do not have enough resources for some minor languages, and it causes less accuracies in minor languages (Kominek and Black, 2006).


Manual annotation and transcription of speech followed the guidelines specified for the GOS corpus (Verdonik et al., 2013).

they were reviewed in order to best meet the needs of ASR.


In this paper we compared the average speaking rate of each corpus by calculating total speech time.

in the case of the Japanese language, although duration of one mora is almost constant, the duration of speech changes slightly depending on the position of the phoneme (Ota, 2003).


Qualitative and reader-task assessments require human readers, professional judgment and experience.

quantitative assessment can be automated, thereby giving the opportunity to explore linguistic features and analyse how they reflect the complexity of the text.


In general, the performance of the algorithms decreased when a new class was added and the best result was obtained by the FilteredClassifier algorithm (0.73 F-measure).

the OneR algorithm 8 got a high F-measure (near to FilteredClassifier algorithm).


Recent works in spoken language translation (SLT) have attempted to build end-to-end speech-to-text translation without using source language transcription during learning or decoding.

while large quantities of parallel texts (such as Europarl, OpenSubtitles) are available for training machine translation systems, there are no large (>100h) and open source parallel corpora that include speech in a source language aligned to text in a target language.


For example, Fisher and Callhome Spanish-English corpora provide 38 hours of speech transcriptions of telephonic conversations aligned with their translations (Post et al., 2013).

these corpora are only medium size and contain low-bandwidth recordings.


In the ELEV domain, NMT had the lead again, as shown in Table 6.

in this case SMT means were closer to NMT's means, which left GT as the worst performing system.


The connection between MT output and "post-edits" is hence weaker than in (Junczys-Dowmunt and Grundkiewicz, 2016) due to the fact that our "post-edits" are actually independent reference translations of the source sentences.

the possible noise introduced by translation errors can only affect one element of our triplets.


Also, we expect the blacklist method to achieve a high precision, because the definition of "blacklist" is actually closely related to literal translation errors.

the drawback of this method is that the method is restricted to only one error type, literal translation errors, and will not detect any other type of errors such as deletions or repetitions of the idiom.


In this example shown in Table 6, the idiom meaning "full of energy" or "actively" is incorrectly translated into "have to".

as this is not a literal translation error, our blacklist method is unable to catch it.


Neural word embedding models trained on sizable corpora have proved to be a very efficient means of representing meaning.

the abstract vectors representing words and phrases in these models are not interpretable for humans by themselves.


One of the most popular semantic resources for English is WordNet (Fellbaum, 1998;Miller, 1995).

wordNet has been criticized for its too high granularity at the bottom level and its generality at the top level (Brown, 2008).


Similarly, the preferences for the fonts that are found to be incongruent by our method was much lower than the expected value, with an average of only 20.13%.

a detailed look at the values for individual emotion attributes reveal that the performance differs between them.


The code of the lexical corrector is available at the above mentioned url 1 .

to standard DL we assign weights to error types: missing or superfluous diacritics only add 0.3 to the distance.


The LSTM-CRF models the prediction task as a classification problem, using a fixed number of non-ordinal class labels.

the LSTM-SIG model provides a continuous prediction, using a sigmoid nonlinearity to bound the prediction scores between 0 and 1.


In these experiments subjects accepted robots with conformed gender and personality to the respective role stereotype more.

to these studies, we choose a more content-related manipulation of personality traits and stereotypes to the robot with verbal cues.


The moderator had the role of leading the discussion.

this does not change the fact that this is an example of dynamic multiparty situated interactions that (Bohus and Horvitz, 2009) define as an open-world dialogue.


The ability to model and automatically detect dialogue act is an important step toward understanding spontaneous speech and Instant Messages.

it has been difficult to infer a dialogue act from a surface utterance because it highly depends on the context of the utterance and speaker linguistic knowledge; especially in Arabic dialects.


For instance, the speaker utters a sentence, which most well expresses his/her intention (act) so that the hearer can easily understand what the speaker's dialogue act is.

the speaker type Operator or Customer of the current utterance can help to determine the act of utterance.


As semantic equivalence is a transitive relation, it suffices that each concept, in a particular language/WordNet, is indexed with an equivalent concept, in any other language/WordNet.

in practice concepts from all languages other than English have been connected to concepts of only one other language, namely English.


With regard to authorization, both approaches use a rolebased access management based on Access-Control-Lists (ACLs).

there are differences in the use of ACLs, and where they are stored.


Game will ask players to listen to short audio clips and identify the language spoken.

our version will improve tracking, language choices, educational potential and, the ability to collect new judgements.


If a dimension were found not to be relevant to any paper in natural language processing, that would constitute evidence that it is not a valid dimension, or at least not a very useful one.

if an analysis of publications in natural language processing resulted in very disparate aspects of papers being lumped into the same dimension, that would be consistent with the hypothesis that the dimension in question needed to be split into finer-grained categories.


More generally, they lie at the very heart of evaluation in natural language processing, where the most common trope is to compare the performance of one system as measured by some figure of merit to that of another (Resnik and Lin, 2010).

with a conclusion, a finding is a repeatable discovery, whereas a conclusion is not-it is instead a broader statement inferred (justifiably or not) from one or more findings.


Findings The finding that the frequencies of explicit phrasal negation in the scientific journal articles were normally distributed was not reproduced.

the finding that the mean of the frequencies of explicit phrasal negation in the scientific journal articles was statistically significantly lower than the mean in the clinical documents was reproduced.


Further, different words can convey affect to various degrees (intensities).

existing manually created lexicons for basic emotions (such as anger and fear) indicate only coarse categories of affect association (for example, associated with anger or not associated with anger).


We find that anger, fear, and sadness words, on average, have very similar VAD scores.

sadness words tend to have slightly lower dominance scores than fear and anger words.


For example, dejected and wistful denotate some amount of sadness (and are thus associated with sadness).

some words are associated with affect even though they do not denotate affect.


Best-Worst Scaling (BWS) was developed by Louviere (1991), building on some ground-breaking research in the 1960's in mathematical psychology and psychophysics by Anthony A. J. Marley and Duncan Luce.

it is not well known outside the areas of choice modeling and marketing research.


The last dimension, Dominance, is sometimes omitted, leading to the VA model.

to NLP where many different formats are being used lexical resources in psychology almost exclusively subscribe to VA(D) or Basic Emotions (typically omitting Surprise; the BE5 format).


Since different from Section 4.2., we now have a fixed test set, we use a one-tailed z-test (p < .05) based on z-transformed correlation values (Cohen, 1995).

to Valence, the performance for Arousal and Dominance may suffer quite substantially in the crosslingual approach, depending on the combination of training and testing languages.


Rather it seems to depend on subtle semantic differences between the translational equivalents of the affective dimensions/categories,  cultural differences, or variations in the annotation guidelines, suggesting that the above assumption of language independence (not so surprisingly) may not fully hold.

to these partly inconclusive results, the outcome for mapping VAD2BE5 is much more favorable for EMOMAP and easy to describe.


As evident from the last section, the performance of our mapping approach may vary depending on source and target language.

different from the last experiment, when constructing new emotion lexicons in a crosslingual fashion, there is no need to restrict the training set to only one language.


We choose Twitter as the source of the textual data we annotate because tweets are selfcontained, widely used, public posts, and tend to be rich in emotions.

other choices such as weblogs, forum posts, and comments on newspaper articles are also suitable avenues for future work.


Similar to the work by Mohammad and Bravo-Marquez (2017b), we create four subsets annotated for intensity of fear, joy, sadness, and anger, respectively.

unlike the earlier work, here a common dataset of tweets is annotated for all three negative emotions: fear, anger, and sadness.


The EI-oc datasets include the same tweets as in EI-reg, that is, the Anger EI-oc dataset has the same tweets as in the Anger EI-reg dataset, the Fear EI-oc dataset has the same tweets as in the Fear EI-reg dataset, and so on.

the labels for EI-oc tweets are ordinal classes instead of real-valued intensity scores.


On the CrowdFlower task settings, we specified that we needed annotations from seven people for each tweet.

because of the way the gold tweets were setup, they were annotated by more than seven people.


As an added benefit, the fastText classifier is dramatically faster than the HypeNET model, with a reduction of training time from around 75 minutes to less than a minute.

as the results of the MaxEnt model show, the features alone are not enough.


NP-MSSG reports the best performance in SCWS where sentential information is available, which shows an advantage of cluster-based models of capturing the senses.

the proposed method significantly outperforms (Fisher transformation at p < 0.05) NP-MSSG and MSSG in RW, MEN and SimLex.


Second, shortened strings, such as abbreviations, co-occur in the same context as their full form but differ strongly in their surface form.

the computational cost of training word embeddings increases proportionally with a rising number of languages considered.


For this reason we chose Jaccard as the baseline for measuring the performance of the proposed embedding-based approach.

linguistic condensation strategies, such as compounding or abbreviations, and the lack of semantic context can pose a serious challenge to string-based ontology alignment methods.


From this point of view, both apple and book have the same role, as both are carrying the [PHYSICAL OBJECT] feature.

this similarity is restricted to the chain clarifying relationship for the verb see.


For limited data, human experts can verify manually the validity of some of them.

our approach, PP, produces hundreds of different paraphrases for each verbal phrase.


Generally in German, punctuation segments a text into sentences, and spaces are used to segment a sentence into words.

this rule of thumb was not always applicable to 1 A sample of our corpus will be available at http:// ansichtskartenprojekt.de Word form: real word forms A2 Normalized word form: all lower case and withoutü A3 Character type of unit: word form is categorised into the following classes: (1) all special characters (2) all numbers (3) capitalized (4) all alphabets without capitalization (5) mixed of all possible character without capitalization A4-7 Suffix: the last 4, 3, 2, 1 character of words, respectively.


The morphosyntactic analysis using the existing POS taggers showed a lower performance, and the semantic features (B) did not achieve high accuracy.

the combination of these three types of features outperformed the word/lemma features.


The main finding was that the window side did not affect the accuracy as much as expected.

the wider context window size slightly improved the accuracy of the test set of TüBa.


These discourse types are typical in postcards but are rarely included in a newspaper corpus.

difference-based entropy is a measurement of differences in entropy scores based on a language model trained on both out-of-domain and in-domain sentences.


This information is frequently needed for research, quality improvement, surveillance, and other important functions.

the manual abstraction of this information can be incredibly timeconsuming and expensive, often making it infeasible for both early-stage research and clinical quality improvement projects.


This implies that a larger volume of entities and relations could be found by processing the same number of words from abstracts than from full papers.

full papers (where available) provide much more information about the entities they contain and so are important for information extraction tasks where important information may not be reported in the abstract.


Therefore, specific training and test datasets are also necessary to precisely translate biomedical document across languages.

despite its importance for the general population and researchers, there are very few parallel and comparable corpora specific for this domain.


The Website site of the Brazilian Clinical Trials Registry 9 provides ways to easily download the trials in XML format, which was further parsed.

given the various elements (sub-sections) in a trial, e.g., inclusion criteria, exclusion criteria, and given that some of these appear multiple times in the document, the automatic alignment of parallel documents is not straightforward.


Sentence segmentation: we relied on tools which are non-specific for the biomedical domains, such as Stanford CoreNLP, OpenNLP and SAP HANA.


Note that this paradigm will only work for MWEs which can be translated into single lexical nodes in the target language; MWEs which are translated by other multiwords will result in translation failures (i.e., insertion or deletion errors).

we expect that such failures will happen relatively infrequently 11 .


They have shown that their translation-based approach performs better than using linguistic approaches.

they did not combine these two kind of approaches.


They compared the translational capabilities of their model with regard to Google Translate.

there was no research specific to the domain of idioms in this work.


(2013) claimed that most of the time user queries need to be addressed with recent information.

many situations demand the past or future related information.


When these two sentences are subjected as input to the SUTime tagger, 2 we observe that, for both the sentences, the word 'present' 3 is tagged as a temporal expression.

it should be temporal only for the first sentence.


Here, both rule-based and machine learning-based methods incorrectly classify the sentence as present.


(1) Businesses are emerging on the Internet so quickly that no one, including government regulators, can keep track of them.

the temporal information of expressions other than "event" also can be a clue to understand text.


In particular, all classifiers we developed share with previous systems basic morpho-syntactic features, such as token, lemma, POS, and dependency relations.

we have added lexical semantic information by using not only WordNet synsets, but also VerbNet classes and FrameNet frames, obtained from the alignments in the Predicate Matrix (Lacalle et al., 2014).


The techniques could be integrated in for instance a reading aid, an activity mainly focused on receptive understanding.

all experts expressed doubt whether language learners, especially high school students, would put in the extra effort to solve the linguistic puzzle laid out before them simply to understand a word, while easier alternatives, such as linked dictionary could be made available.


But most of the web texts retrieved by search engines require a high language proficiency, even for native speakers (Vajjala and Meurers, 2013).

the use of an off-line corpus ensures content quality, while hindering the search for different text topics.


One remarkable extension in version 4.0 was the inclusion of a Coreference Resolution module, based on Relax-Cor, the second-ranked system in CoNLL-2011 shared task 1 http://nlp.lsi.upc.edu/freeling 2 http://www.gnu.org/copyleft/agpl.html (Sapena et al., 2011).

academic shared tasks have a very specific scenario, which does not necessarily match the real-world settings in which a system like FreeLing is required to operate.


In cases where they do not refer to the same referent, but a related entity, they can in principle be considered bridging.

in this case, Tehran is not anaphoric, which leads us on to the following important distinction.


in a case like http://www.ims.uni-stuttgart.de/ institut/mitarbeiter/roesigia/ guidelines-bridging-en.pdf where some people might consider this a bridging case, as the foreign secretary Mottaki is probably not interpretable alone for a typical WSJ reader without the mentioning of Iran first.

others might argue that his discourse referent might already be identified by his name.


The first improvement featured larger word embedding vectors (of the size 300 instead of 50), which gave 6647 features for each training example.

despite much richer embeddings, we did not observe any significant improvements in the evaluation metrics.


A small dataset with manual coreference annotation was earlier published for Hungarian (Miháltz, 2012).

here we present our large corpus, SzegedKoref, which has been manually annotated for coreference data.


The coreference relation is shared across all languages.

languages differ considerably in the range of linguistic means triggering this relation (Kunz and Steiner, 2012;Kunz and Lapshinova-Koltunski, 2015;Novák and Nedoluzhko, 2015).


adverbs are not considered in most coreference annotation schemes.

they constitute around 8% of all referring expressions in the German language 1 and are especially frequent in spoken and spoken-like language.


We suppose that the reason for the greater disagreement for German texts is the complexity of the linguistic structures triggering coreference in this language.

a more detailed analysis of the agreement results is needed to understand the reasons.


4 million parameters on as little as around 100 hours of speech.

the de facto standard in the industry is to use sizeable training corpora, which total durations are measured in hundreds or even thousands of hours (Amodei and others, December 2015; Xiong and others, January 2017).


The present discussion has focused on the collection of a large quantity of continuous speech recordings.

our first attempts at recorded speech collection were focused on a much simpler case, i.e.


For example, the database at National Institute for Japanese Language and Linguistics (National Institute for Japanese Language and Linguistics, 2016) includes text and speech data spoken by native dialect speakers.

the recording setting is not suitable for spoken language processing.


In addition, because their corpus includes the parallel text of the common language and dialects, it is also useful for natural language processing research.

dialects that can be collected in such a perfect environment are very limited and collecting many dialects is unrealistic because of geographical and expense issues.


Vocabulary knowledge prediction is an important task in lexical text simplification for foreign language learners (L2 learners).

previously studied methods that use hand-crafted rules based on one or two word features have had limited success.


Another one, Page Analysis and Ground-truth Elements(Pletschacher and Antonacopoulos, 2010) (Page XML) allows to store image features, layout structure and content page.

the diversity and complexity of data contained in a daily page of Italian comedy require types and tags to be more specific and hold-back all levels of annotation.


In both cases, the textual data directly describes image content, thus enabling the above-mentioned lines of research.

these datasets are often restricted to English language text and typically of relatively broad domain; The FLICKR caption datasets for instance contain images including landscapes, animals, and everyday scenes while the COCO dataset is similarly broad but contains more items per image.


On the one hand, we will include other types of fashion items besides dresses, such as shoes and shirts.

we aim to repeat crowdsourced data gathering efforts for languages other than German, such as English, French, and Dutch.


In crowdsourcing, since difference how much preserve meaning for each annotator, the case that BLEU become low occurs.

it is considered that the quality of simplification corpus is high because about 70% of inter-agreement exceeds 0.4.


The aligned files (parallel corpus) share the same first part of the file name.

not all files with the same name contain the same amount of text.


Also, many annotators chose to mark a minimum of entities instead of marking all options.

this manual inspection showed us that the topics that were marked, were correctly marked, linked and also correctly linked to the topic in the target language (in this small sampling 90-100% correct).


An approach based on ontology label translation (Arcan and Buitelaar, 2013) was developed to provide a knowledge-based extension to a statistical machine translation system.

automatically translated multilingual terms often suffer from quality issues.


On the one hand, it exhibited a high frequency of domain specific terms and expressions, named entities, scientific formulas, and words unknown to crowdworkers, as well as to any system for posterior processing.

the subtitle genre contained spontaneous speech properties, truncated sentences, elliptical formations, disfluencies, repetitions, interjections and fillers.


This method of collecting vocabulary-test results may be beneficial for classroom teaching because the environment under which a dataset is taken is a classroom and the applications to which the dataset are used are also for classrooms.

this is not the case for developing educational software, in which participants are more diverse than in typical classrooms.


This feature has been used particularly with the ACE corpus.

modern corpus is no longer annotated with this feature, which is why we do not include it.


Generally speaking, only the factual information with high credibility has the value for use.

most expressions in social media are published with the hypothesis and episteme which cannot be decided whether it is true or false at that moment.


Similar work for Chinese has been reported by Ji (2010), who constructed the Chinese corpus from newspapers for Chinese uncertainty identification.

their data was annotated exactly based on the cue-phrases and size of corpus was a bit small for some uncertainty detection systems.


Question is the uncertainty category with the most microblogs, followed by Possible.

only 117 microblogs are labeled as External.


As meta-data for describing event information, event types and schemas are very important information in many applications such as event extraction.

there is no large amounts of annotated data for event typing or event schema extraction.


In some sense, this data can be seen as a small knowledge base of air crashes.

our EventWiki contains much more major events of a variety of types and rich knowledge about events, which is the first complete event-centric knowledge base as far as we know.


Strictly speaking, spin is not necessarily a form of deception, as the intention is difficult to establish most of the time, e.g., spin in abstracts may be conditioned by limited space; by author's wish to report the results that he/she perceives to be most important; by unclear/absent reporting guidelines; by lack of training etc.

spin is similar to deception for what concerns its impact and the method required to detect it from textual content only (Mihalcea et al.


The goal of those previous location estimation researches is to estimate the locations vaguely with best-effort approaches.

our goal is to provide the positions of infected people as accurately as possible.


This has been showcased in cross-genre experiments in Section 4, which also show that our model, trained on tweets, is robust and achieves results comparable to the models built specifically for the crossgenre task.

specific components and the entire workflow can be reused for building new models for AP as well as for other text classification tasks.


Fast prototyping is important for the users who wish to develop new models quickly.

there are users who just wish to use an already developed and trained model.


While thus in the computational linguistics literature, word pair relations have been extensively researched, a focus or look to the categories function vs. content word is rather rare.

(psycho-) linguistic literature often applied these categories, but a focus on word pairs is rather rare.


Since cross linguistically, the structures of synonymy (and ultimately word nets) seem to differ to certain extents, it seems plausible that interindividual differences to similar extents can exist within speakers of the same language.

function words and functional morphemes such as regular endings are rigid and shared between speakers as to the mapping of form and function.


Ideally, publications that report on a result of an empirical study should contain a direct link to the cited dataset and lead the reader directly to the research data that underlies the publication.

in practice, this metadata is often missing.


The problem of automatically linking a data citation text fragment to the corresponding dataset has been addressed 1 https://wosp.core.ac.uk/ 2 Workshop on Research Results Reproducibility and Resources Citation in Science and Technology of Language in the INFOLIS project (Boland et al., 2012).

advanced algorithms that are able to identify the survey variable mentions used in the underlying study and link them to a specific survey variable identifier in a knowledge base are still lacking.


A design decision was to restrict the length of text samples to a sentence.

in the variable corpus, the local context of the variable is provided, i.e., the whole paragraph in which the mention occurs, so that the similarity of the context of the mention with the associated variable can be exploited.


Thus, temporal tagging has become a vibrant research area, and several new temporal taggers have been made available and new strategies have been developed.

as was shown in previous work (Mazur and Dale, 2010;Strötgen and Gertz, 2013;Bethard et al., 2016;Tabassum et al., 2016), different types of documents pose different challenges for temporal tagging such that domain-sensitive normalization strategies are required (Strötgen and Gertz, 2016).


To judge the performance of temporal taggers and new methods, evaluations need to be performed on diverse text types, e.g., on news articles and narrative-style Wikipedia documents.

to many natural language processing tasks, there has also been some effort towards multilinguality in the context of temporal tagging, e.g., research competitions were organized not only for English but covered further languages such as Spanish and Italian (Verhagen et al., 2010;Caselli et al., 2014).


They have then been adapted to other languages such as Italian (Caselli and Sprugnoli, 2015), Spanish (Saurí et al., 2009) and French (Bittar, 2010).

until now no adaptation of the guidelines to German has been done.


named entity recognizers, syntactic parsers, semantic role labelers) are widely used -typically many such components in combination -as inputs to a more specialized (and complex) application.

the process of managing and aggregating these tools and their inputs and outputs is typically labor-intensive and errorprone, requiring significant engineering effort before the target application can be built and evaluated.


Our main targets are data centered workflows, hence the explicit input and output field in the metadata scheme described in the next section.

it is noteworthy that the scheme is flexible enough to also model steps consisting of only title and description, e.g.


The main problem of Bokeh is that it is not NLP centric and too general, and its API is rather abstract to be used directly.

it can be used as a low-level graphing API for a custom layout engine as well as a basic WebUI.


Web-scale information extraction systems like NELL (Carlson et al., 2010) or Knowledge Vault (Dong et al., 2014) can acquire massive amounts of machine-readable knowledge from the Web, whereas projects like DBpedia (Bizer et al., 2009), YAGO (Rebele et al., 2016) or BabelNet (Navigli and Ponzetto, 2012) have turned collaboratively-generated content into large knowledge bases.

all of these resources are entitycentric in that they are primarily built around the notion of entities, as either provided by an external resource (e.g., Wikipedia pages) or automatically discovered from text (e.g., by clustering entity mentions).


saneren "remediation" as hypernym of saneren van verontreinigde bodems "remediation of contaminated soils".

this account would not suffice for constructions requiring the more local noun to be the hypernym.


In their paper, they argue that the logical entailment present between the text and hypothesis is not captured properly.

we are interested in a subtype of entailment that can predict the support relation based on argumentation theory.


Using our system we achieved a significantly higher accuracy score of 78.3%.

as there were no dedicated test set for this dataset we tested our system on a randomly selected subset of 20% of the data, which makes the comparison with the other systems not conclusive.


As noted before, Twitter has been the most common source for sarcasm in previous corpora; this is likely due to the explicit annotation provided by its hashtags.

using Reddit as a source of sarcastic comments holds many research advantages.


The third case amounts to solving word sense disambiguation, and we did not find a universally simple approach to reduce noise of this form.

it is possible to reduce the likelihood of this form of sense mismatch by restricting to subreddits which are known to not have alternate senses for "/s" (e.g., politics).


We also experimented with instructions that do not include negative examples.

apart from some reduction in the number of low-quality happy moments, we did not detect significant differences between happy moments that are collected from instructions with or without negative examples.


Code-switching can be observed at various linguistic levels of representation for different language pairs: phonological, morphological, lexical, syntactic, semantic, and discourse/pragmatic switching.

very little codeswitching corpora exist from which researchers can train statistical models.


In grammatical error correction, often a more detailed taxonomy for errors is used; the default benchmark has 28 categories (Ng et al., 2014).

many of the errors in this taxonomy are not annotated in the normalization benchmarks and many normalization replacements are not included in this taxonomy.


(2015) are also annotated with error categories.

the guidelines for the annotation of these corpora are substantially different compared to the other, more commonly used, corpora.


The taxonomy proposed by Baldwin and Li (2015) has a very high percentage of normalizations since it allows deletion and insertion of tokens as well as the correction of capitalization.

the Foreebank (Kaljahi et al., 2015) has a very low percentage of normalized words.


Thus, based on our analyses of the group discussions we further conclude that the hypothesis "Individuals who have a high degree of influence within a group discussing a topic they are knowledgeable about, remain influential when moved to another group that discusses a different topic they are less knowledgeable about" is not supported by the data that we have.

we have shown that when influential people are moved to groups where unfamiliar topics are discussed, their influence declines, sometimes significantly.


All these features can be useful for learner corpora transcription.

as the final users of the digital texts produced via the above mentioned crowdsourcing tools are historians, the annotation systems of these tools are adapted to bibliographical, paleontological and historical needs.


For instance one can use some language model and then let some algorithm such as Viterbi find the most probable final text.

this will only produce variants already extant in the manuscripts, but not such, which have been ancestral, but irretrievably altered.


As denoted previously in Section 1., the number of cuneiform character classes are up to hundreds.

most of them are ligatures of basic characters or less frequently appeared.


As denoted in Section 2.1., we limited target age for collection.

for constructing more practical dataset, we have to collect other glyphs of other ages.


If the stimuli which are presented to crowd-workers are based on acted scenarios, the reactions of crowd-workers might also not be as they would be in a completely spontaneous co-located interactions.

in a completely spontaneous co-located interaction, participants might also be influenced by other events or actions going on at the same time.


Finding new technological solutions in order to enhance the work in clinical operating rooms has been in the focus of research for many years.

with the emergence of new technologies, the surgical working environment comprises nowadays many medical devices which have to be monitored and controlled and thus becomes increasingly complex.


When the surgeon confirms to start with the surgery, the second part of the dialogue begins.

to the first part which is very flexible and allows the user to control the dialogue, the procedural part follows an exact surgery schedule which has been modelled in the Spoken Dialogue Ontology.


Moreover, IDACO stays in the background if the procedure goes as scheduled.

the evaluation with the experienced physicians indicated that passive system behaviour makes the surgery team insecure.


Especially, corpora of free conversations annotated with some linguistic information are valuable.

for the Japanese language, there is no annotated corpus in the domain of free conversations that is publicly available.


• The "Sympathy" tag can be assigned when an utterance shows sympathy or approval.

if a speaker just shows agreement with the other participant, the "Sympathy" tag is not assigned.


F rw1 simply checks whether the same content word appears in both the current and the previous utterance.

f rw2 more strictly checks the presence of a repetition of content words: f rw2 is activated if either of the conditions below is fulfilled.


location), the dynamic nature of RL can be better studied, similarly to how Ibarra and Tanenhaus (2016) observed changes in referring strategy when contrastive features previously used to disambiguate entities are no longer effective due to introducing new entities with similar features.

these tasks culminate to an end goal, again leading to e.g.


t n and treated each unique referent in the corpus r ∈ R as a "document", where |R| = 840   Table 7: Significance of the correlation between coreference sequence order n (the n th time a round in a game refers to an entity with a unique SHAPE value s) and instructor token type overlap ∆c s n .

for |D| = 42 dyads with 20 referents per dyad: (2) in order to encode the knowledge that RL converges in dialogue (see Section 5.1.


In this endeavour, they use word vectors in combination with deep neural networks to determine the dialogue act of an utterance.

the representation in vector space they utilise stays on the word level.


The original word2vec trains its representations similarly to autoencoding.

rather than training against the input word itself, word2vec trains words against their adjacent words in the input corpus, either using the word to predict its context or using the context to predict the word.


Considering only those results, employing LCs would seem unnecessary.

their advantage of providing potential for generalisation becomes prominent when a DVM that was trained on one corpus is used for clustering utterances of the other corpus.


In other words, the perception of strong engagement is triggered by the expert's smile.

for the partially disengaged level, the mean starting time of smile is 1.5 seconds: smile is produced 1.5 seconds after the perception of a partial engagement.


The notion that addressees are indicated to a lesser degree in the text thus seems implausible, given that the features applied to addressees show a larger change compared to speakers.

if we consider the difference in the distribution of types presented in Table 3 for speakers and  Table 4 for addressees, we see that there are significant differences for explicit, anaphoric and definite description indicators.


Direct comparison with previous work is difficult, primarily because of differences in the data-size and the experimental setup.

our results on speaker identification are relatively similar to previously obtained results (Elson et al., 2010;He et al., 2013;Muzny et al., 2017).


Parallel corpora play an important role in many multilingual NLP applications, such as Machine Translation, Cross-Lingual Text Classification or Information Retrieval.

the topics and genres of parallel corpora are limited even for better resourced languages, e.g., resources are scarcer outside of the official documents of Europarl and the United Nations (Koehn, 2005;Eisele and Chen, 2010).


In addition to a model with a seed bilingual dictionary, it also introduced constraints on what its authors call "morphological structure" (actually the Levenshtein Distance) for keeping only the cognate words in the output.

further work on bilingual lexicon induction did not include the use of cognates, especially in the context of related languages.


The potential for such one-to-many matches is smaller for closely related languages, since they usually have the same set of morphological categories.

differences in the suppletivism of forms are common even across related languages, for example, the feminine adjectival forms ending with 2 https://github.com/ssharoff/cognates ой in Russian (e.g., новой, 'new') are used for any nonnominative case, while unique cognate forms are used in Ukrainian for each grammatical case, e.g., genitive: нової, dative: новiй, instrumental: новою, etc.


For example, the meaning of the identical forms postale in both French and Italian is the same ('post.adj'), they share a number of collocates with the same meaning, e.g., adresse postale vs indirizzo postale, so they are likely to be well-aligned in the shared embedding space (either with or without WLD constraints).

the French form is feminine, while the Italian one is masculine, so the correct embedding space should have mapped postale in Italian with postal in French.


Even as NMT development proceeds at breakneck speed, research on newer advanced technologies based on Quantum Neural Networks (QNN) is already in progress (Moire et al., 2016).

despite of the significant improvement in translation quality, the ability of NMT systems to correctly translate named entities and some technical terms has in fact somewhat deteriorated.


Some potential obstacles are (1) that lexicons, unlike corpora, do not provide context, and (2) that ordinary lexicons do not provide translation probabilities.

this is not critical for named entities, especially POIs, and even for many technical terms, since named entities are mostly monosemic, which means that word sense disambiguation is unnecessary and that the lexicon can automatically be assigned a higher probability.


A larger vocabulary indeed means being able to understand a larger scope of texts.

analyses of large corpora indicate that, from kindergarten through college, native speakers encounter approximately 150,000 different words (Zeno et al., 1995).


At C2, a learner is able to read virtually all forms of written language however abstract, structurally or linguistically complex.

such descriptions remain elusive and the limitations of the CEFR for practical purposes have been stressed (North, 2005, 40).


The methodology applied has the great advantage of being able to assign different difficulty levels to the different senses of a word.

alderson (2007)   which is a collection of performances on Cambridge examinations, may be an issue for generalization.


It also offers a finer view of word use within a level: for instance, EFLLex makes obvious that write is a much more prevalent word at A1 (934 occurrences) than explore (20 occurrences).

the resource also has some limitations as regards frequency estimation.


NMT has outperformed previous translation systems in many language pairs (e.g., German-English, French-English).

in order to reach high accuracies, neural translation systems tend to require very large parallel training corpora (Koehn and Knowles, 2017).


Several ideas have been proposed in order to mollify this issue including multi-lingual systems with zero-shot translation (Johnson et al., 2016), transfer learning (Zoph et al., 2016) and back-translations (Sennrich et al., 2016).

their general effectiveness still requires wider evaluation.


For the NMT model, updating the pre-trained embeddings during training (u-emb) has invariably led to the highest accuracies, up to an improvement of 2.98 BLEU points over the random embeddings in the eu→en direction.

the performance ranking has changed drastically when testing on the more probing Berriak corpus.


On the other hand, the training corpus of Google Translate is certainly much bigger, and that has helped it achieve better results on Berriak.

the BLEU score when Basque is the target is still very low (9.91) and significant improvements are an outstanding need.


Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years.

the under-translation and over-translation problem still remain a big challenge.


The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT).

there exists discrepancy between training and inference in NMT when decoding, which may lead to serious problems since the model might be in a part of the state space it has never seen during training.


In many applications of sequence-to-sequence models, at inference time, the output of the decoder at time t is fed back and becomes the input of decoder at time t+1.

during training, it is more common to provide the correct input to the decoder at every time-step even if the decoder made a mistake before, which leads to a discrepancy between how the model is used at training and inference.


Traditionally, backing-off language models rely on the n-gram approximation, which are often criticized because they could only store limited information and thus lack any explicit representation of long range dependency.

recurrent neural network language models always estimate probabilities based on the full history (Sundermeyer et al., 2012) In other words, recurrent neural networks do not use limited size of context, which is the major reason why we choose recurrent neural network language models.


It can be seen from the figure that, if very similar sentences (0.4-1.0) can be found for the testing sentence, using only 1 similar sentence can greatly improve the performance, using more does not provide further help and may even degrade the performance.

when the found sentences are not very similar (0-0.4), the improvement brought by fine-tuning is much smaller, and using more sentences, such as 16, is better than using one.


(2012) proposes a local training method which also learns sentencewise weights based on similar sentences.

since there are only about a dozen of features in SMT, such as translation score and language model score, adjusting the relative weights of these features cannot making full use of the similar sentences.


Much work has been done on machine translation between major language pairs including Arabic-English and English-Japanese thanks to the availability of large-scale parallel corpora with manually verified subsets of parallel sentences.

there has been little research conducted on the Arabic-Japanese language pair due to its parallel-data scarcity, despite being a good example of interestingly contrasting differences in typology.


Much work has been done on MT between major language pairs including Arabic-English and Japanese-English, thanks to the availability of large-scale parallel corpora across various domains with manually aligned subsets.


In addition, Arabic has a complex system of derivation, inflection, and cliticization.

a Japanese token can be highly ambiguous due to the absence of spaces between tokens.


For the French translation of outward investment, the most used translation within the parallel corpus is investissement extérieur, whereby the additionally suggested term investissement réaliséà l'étranger documented in IATE does not appear in the corpus.

the French translations of the term electrical engineering, i.e., electrotechnique and génieélectrique, are both frequently mentioned in the used corpora.


Many other languages exhibit similar morphological patterns that could not be handled by Unidecode.

the baseline actually performs better than Moses in 42 languages, which is a testament to the difficulty of our task.


In language modeling, perplexity or cross-entropy is widely accepted as a de facto standard for intrinsic evaluation.

distributed word representations include the additive (or compositional) property of the vectors, which cannot be assessed by perplexity.


We gave examples of similarity in the task request sent to annotators, so that we reduced the variance of each word pair.

we did not restrict the attributes of words, such as the level of feeling, during annotation.


Kipper Schuler's (2005) Verb-Net, grouping English verbs into classes defined by shared meaning components and syntactic behaviour, is one of the richest lexical verb resources currently available, and its utility in various NLP applications has been repeatedly demonstrated (Rios et al., 2011;Windisch Brown et al., 2011;Schmitz et al., 2012;Lippincott et al., 2013;Bailey et al., 2015).

creation of a similar resource from scratch, drawing simultaneously on semantic and syntactic criteria, is a challenging and time-consuming task when attempted by annotators without theoretical linguistics background (Majewska et al., 2017).


The result for the first baseline, 0.0, is the same as in SemEval, and a natural consequence of B-Cubed since there are no pairs within a class.

while the overall performance of the All-instances, One sense baseline in SemEval surpasses its best participating system (achieving the score of 0.623), the result for this baseline on our verb clustering is much lower (0.069), suggesting the task is significantly more difficult, due to the high number of clusters.


Another type of multi-sense word embeddings introduce external knowledge base for accurate sense generation (Iacobacci et al., 2015;Chen et al., 2014;Pelevina et al., 2017).

they are somehow limited as such external knowledge may be lack for languages other than English.


Mainstream approaches employ machine learning techniques to integrate/combine visual features with linguistic features.

to or supplementing these approaches, this study assesses the effectiveness of social image tags in generating word embeddings, and argues that these generated representations exhibit somewhat different and favorable behaviors from corpus-originated representations.


The table presents that the degradations in USF Assoc score compared to that of SimLex999 are evident in all the embedding types.

the difference in YFCC (tag embedding) is larger than the other two types.


For example: evaluate if a model for a frame F, trained on data from the archeology domain can successfully be applied on data from the WW1.

to full text parsing corpus, the frame semantic annotations of CALOR are limited to a small subset of frames from FrameNet (Baker et al., 1998).


Therefore the ambiguity is limited and CRFs can be trained efficiently even with a large number of features.

the drawback is that the training data is split across words in the LU lexicon, therefore similarities among LU are not exploited.


Experiments show that biLSTM-MT model achieves a better recall, while CRF-MM achieves better precision, this is due to the architecture of each model, in CRF-MM we divide frame parsing into small subtasks one per LU, reducing the number of possible labels in each decision, thus augmenting the precision.

biLSTM-MT is able to share data across LUs boosting its capacity to deal with complex syntactic patterns and being able to retrieve more frame elements during parsing.


Charteris-Black (2009) collected data of British parliamentary debates from online versions of Hansard, while Koller and Semino (2009) assembled a corpus of interviews and speeches by Germany chancellors from the official websites of the German government.

these corpora have not been released and made available for public search or free use.


For example, Lu and Ahrens (2008) found that Kuomintang Presidents in Taiwan used BUILDING metaphors to instill a Chinese ideology.

the president from the Democratic Progressive Party preferred not to use BUILDING metaphors, and instead used FARMLAND metaphors to emphasize Taiwan's agricultural background and political independence.


Usually, the Japanese sense dataset Balanced Corpus of Contemporary Written Japanese (BC-CWJ) (Maekawa et al., 2014), tagged with sense IDs from Iwanami Kokugo Jiten (Nishio et al., 1994), is used for supervised WSD.

unsupervised approaches to allwords WSD often require synonym information, which sense datasets cannot provide.


If the synonyms for Sense 1 are A, B, and C, and the synonyms for Sense 2 are C, D, and E, we exclude C from the synonym sets for both Sense 1 and Sense 2.

the above conditions can not take into consideration the ambiguity of the synonyms.


The original SemEval 2010 Task 14 used the V-Measure external clustering measure (Manandhar et al., 2010).

this measure is maximized by clustering each sentence into his own distinct cluster, i.e., a 'dummy' singleton baseline.


Indeed, most of sense annotated corpora are either directly annotated with WordNet sense keys or they are annotated with a sense inventory linked to the senses of WordNet, such as BabelNet (Navigli and Ponzetto, 2010).

it is not trivial to use these corpora, because most of them differ in their format and on the version of Word-Net they use.


Sense annotations have been converted, when necessary, from their original WordNet sense key to the last version of WordNet (3.0) thanks to conversion tables from (Daudé et al., 2000).

because some senses have been dropped from the old versions of WordNet, some sense annotations have not been converted.


Word embeddings -generated with neural networks (NN) or other factorization techniques -are a standard element in natural language processing (NLP) applications.

an important issue is their lack of sense-awareness, i.e.


Obviously, fastSense outperforms these competitors.

since these tools link to different resources (e.g., DBpedia, WordNet or Wikipedia), this comparison only holds for time effort.


Of course, the tree representation removes the context from which the events are extracted.

by presenting both views at once, the user is able to see the summary semantics of the annotation relations alongside the annotations embedded in the text.


So far, we have shown how the same concept can be represented through different images, depending on perspective, or the semantic content highlighted (Faber et al., 2007;Reimerink, García de Quesada & Montero-Martínez, 2010).

one and the same image may also work for the representation of other related concept entries (e.g.


Thus, each annotator can freely describe the function the VKP represents in this image.

as this will probably cause a high degree of inconsistency between annotators, a list with fixed options has been defined, which will be implemented shortly (see Table 1).


The WebANNO tool is also a web-based tool that offers wide range of linguistic annotations tasks, e.g., named entity, dependency parsing, co-reference chain identification, and part-of speech annotation.

both systems SWAT and WebANNO lack of some functionalities and features that can simplify and speed up the annotation task for our purposes.


This architecture design allows multiple annotators to work on various tasks simultaneously.

wASA allows the admin user to manage and handle a single central database.


To increase the speed of the annotation process, some of the words, like Named Entities and punctuations, will have an initial tag assigned automatically as part of a preprocessing step.

the annotator is allowed to change the initial tag if he/she finds words annotated with a wrong tag.


His findings showed that, for example, in Japanese novels, words that are highly characteristic are suffixes and nouns attached to people, such as san, sama, chan (all are general suffixes of personal names but differ in politeness).

in translated novels, many pronouns and proper nouns are highly characteristic.


For PDF annotation, there are many commercial products such as Adobe Acrobat, PDF Annotator 1 , and A.nnotate 2 , which basically support text highlighting and adding notes and comments on PDF.

these tools are not intended to be used for linguistic annotation, thus these lack annotation types suitable for linguistic phenomena such as dependency relation and coreference chain.


However, these tools are not intended to be used for linguistic annotation, thus these lack annotation types suitable for linguistic phenomena such as dependency relation and coreference chain.

pDFAnno supports such relation annotation and multi-user annotation.


Actually, every annotator performed the work of textbased annotation while referring to the original PDF.

such problems did not occur in the PDF-based annotation.


This can be helpful for data exchange between collections of predefined processing components such as parsers.

it made this approach unsuited for us as alternative to designing our own modeling toolkit.


Several ontologies have been built for the representation of legal concepts (Hoekstra et al., 2009;Wyner and Hoekstra, 2012), and there are formal languages for the representation of the content of legal documents, such as SBVR (OMG, 2008) and LegalRuleML (Athan et al., 2015).

a direct translation from natural language, particularly legal language, into a given formal language is extremely difficult to accomplish.


On the one hand, legal professionals cannot reasonably be expected to engage with the complexity of LegalRuleML conformant encoding of legal documents.

it is important to keep track of the text which has been translated.


Repositories of linguistic annotation terminology, such as GOLD (Farrar and Langendoen, 2003), ISOcat (Windhouwer and Wright, 2012) and its successor CCR, 3 make it possible to overcome the heterogeneity of annotation schemes by acting as an interlingua that allows mapping annotations from one scheme to another, thus addressing conceptual interoperability (Chiarcos, 2012a).

far from all corpora that we use today follow the principles mentioned above.


(2005), who discuss the issues involved in creating a Unified Linguistic Annotation (ULA) by merging the annotation schemes of PropBank, NomBank, Time-Bank, the Discourse Treebank and Coreference Annotation.

their work remains on theoretical ground by limiting their discussion to overlapping and conflicting annotations in example sentences.


For all three datasets, the verbs that were not marked as events were mostly the verbs be, have and do, which we assume to be those occurrences where they act as auxiliaries (91%, 65% and 68% of non-annotated verbs in PB/NB, FB and TE3 respectively).


The coverage of adjectives, prepositions and adverbs is quite similar on the three datasets, as shown in Table 3.

we know that for PB/NB these are all part of phrasal verbs (cut loose, dig up), which also accounts for the high coverage of particles in PB/NB.


The authors of Elephant (Evang et al., 2013) made their system available at http://gmb.let.rug.nl/ elephant, 4 and deserve our gratitude for making the effort to provide a clear documentation, including how to reproduce their experiments.

although a training script is provided, this script only allows training the CRF model.


): a CRF model is trained, which might include features from a RNN language model previously trained with the same training data.

instead of using only one specific template for the CRF model, the cross-validation stage described in §4.2.


If E and J were in perfect harmony then we would be able to pair e 1 with j 1 , e 2 with j 2 and so on.

matched documents are rarely in such close correspondence.


Middle Dutch shares a number of interesting phenomena with Early Modern Dutch (e.g., case marking, clitics, pronoun compounding) which are not found in Modern Dutch, therefore this tagger provided a useful starting point for manual annotation.

major differences between the two historical language varieties exist as well, which necessitates a full check on all generated tags.


To extend this approach, in Figure 1 agreement measures are provided 1 for lemmas, full POS tags, main tags only, agreement on single features instead of feature sets, and agree-1 Originally, the tagging task was assigned to a pool of nine annotators, each of which was assigned an individual set of documents as well as a selection of documents from the set of the previous annotator for the measurement of inter-annotator agreement.

one of the annotators (annotator e) left the pool, which means that the agreement for the pairs d-e and e-f could not be measured, resulting in a total of seven pairs in Figure 1.


Therefore, the established categories were selected as a framework for sociolinguistic tagging of the current corpus.

given the fact that agreement on letter goal and topic is low, the framework may need to be revised.


In recent two decades, a number of scholars have focused on Chinese word extraction for solving CWS or CCWS by dint of various association metrics or a hybrid model of several metrics (Chang and Su, 1997;Chen and Ma, 2002;Luo and Sun, 2003;Ma and Chen, 2003;Feng et al., 2004;Tang et al., 2009;Zhang et al., 2009;Zhang et al., 2010;Duan, Han and Song, 2012;Zhang et al., 2012;Mei et al., 2015;Shen, Kawahara and Kurohashi, 2016).

one big problem for collocation-based methods is that the performance of certain systems will heavily depend on the thresholds setting, because the thresholds for such association metrics are always set heuristically or empirically to gain high performance, which means we have to adjust thresholds with the change of applications or domains with extra effort.


The word is a Chinese idiom of which the definition is included in Baidu Baike.

with the statistical information alone, this 4-gram word is not extracted out of the corpus.


A lot of the work in essay grading today makes use of the ASAP AEG dataset.

most of the essays only have an overall score, not attribute-specific scores.


Most of the essays were annotated by a single annotator.

about a sixth of them were annotated by a second annotator.


Exercise related advice are relatively rare (n=40), as exercise is often recommended for most of the chronic diseases.

it is found from the annotation that exercise in certain contexts (e.g., in a hot or humid weather, within certain time range of drug administration) can negatively affect well-being.


Persuaders who express positive emotions increase the ratio of success at persuasion more than persuaders who do not express their emotions because expressing positive emotions gives a cooperative impression to a partner (Carnevale and Isen, 1986;Forgas, 1998).

expressing negative emotions such as "anger" may wrest a concession from a partner even if the proposal from the persuader is not attractive to the partner, especially when the partner does not have any other options to choose (Sinaceur and Tiedens, 2006).


Emotional expressions tend to be observed in communications between people who have close relationships.

it is hard to record dialogues in such closed situations.


Automatically detecting emotions has also gained considerable attention over recent years, especially from text (Mohammad, 2012b;Mohammad, 2012a;Zhu et al., 2014;Kiritchenko et al., 2014;Yang et al., 2007;Bollen et al., 2009;Wang et al., 2016;Mohammad and Bravo-Marquez, 2017; but also from images (Fasel and Luettin, 2003;De Silva et al., 1997;Zheng et al., 2010).

image annotations for emotions have largely been limited to small datasets of facial expressions (Lucey et al., 2010;Susskind et al., 2007).


Which Emotions Apply Frequently to Art: Humans are capable of recognizing hundreds of emotions and it is likely that all of them can be evoked from paintings.

some emotions are more frequent than others and come more easily to mind.


On the CrowdFlower task settings, we specified that we needed annotations from ten people for each instance.

because of the way the gold instances are setup, they are annotated by more than ten people.


(Fleiss' κ calculates the extent to which the observed agreement exceeds the one that would be expected by chance (Fleiss, 1971).

note that correcting for chance remains controversial.


5 Established in 2013, this series of health-related challenges led to the preparation of several corpora-mostly for the English language, but also for other European languages.

these corpora are typically very small and available only for usage directly related to the respective task, i.e., they can neither be used later on nor are they available for the research community independent of the specific CLEF task.


Recent neural domain adaptation approaches also work through cross-domain embeddings to improve the cross-domain performance (Cai and Zhao, 2016;Zhang et al., 2014).

a critical examination of the underlying assumption, and their assessment in the light of naturally occurring linguistic data, reveal its inherent contradictions (Taylor, 2012).


In its features, it mainly follows HunToken 4 , which is a rule-based tokenizer and sentence boundary detector for Hungarian (and English) texts.

emToken differs in several properties, e.g.


Previous morphological analyzers for Hungarian used various ad hoc tagsets.

the tagset used by emMorph and emLem contains tags suggested in the Leipzig Glossing Rules (Comrie et al., 2008) widely used by linguists.


The GUI-based framework of the LCM enables end-users to conduct standardized text mining workflows without any programming skills.

individual and innovative research designs often demand more flexibility which is hard to achieve with generic pre-defined workflows accessed by point-and-click GUIs.


The LCM is optimized for the generic processing of large amounts of text data.

for experienced researchers, it is easy to identify needs for analyzes that go beyond the generic usage of text mining.


Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms.

building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models.


(b) No query options: To extract information from UIMA documents, these documents must first be completely imported into the cache.

it is not possible to select only those UIMA documents that are required for a given application.


For all six systems, morphosyntactic homographs like read, live, and lives are more challenging than lexical homographs like bass.

the server model performs significantly better on morphosyntactic homographs than the embedded model, presumably due to the presence of POS tag features.


These issues could be caught by carefully inspecting the intermediate data between every normalization step for every new model trained.

we avoid this approach for three reasons: privacy considerations; the sheer number of language models across languages (each one with many data sets that undergo many steps of normalization); and wanting staff to be able to develop improvements to models even when they do not speak the language involved.


() built a dataset of 868 German noun-noun compounds, where one of the annotations quantifies the compositionality of the compound on a scale of 1 (semantically opaque) to 6 (semantically transparent).

to Kruszewski and Baroni (2014) and Schulte im Walde et al.


The two texts in 1a and 1b are textual paraphrases 1 .

they include more than one atomic paraphrase': "magistrate" and "judge" are an instance of "same polarity substitution", while "A federal magistrate ... ordered" and "Zuccarini was ordered by a federal judge..." are an instance of "diathesis alternation" 2 .


In a similar way, in 11b, "boats" has the meaning as "all boats".

in 11a, "boat" can have the meaning "one particular boat", thus the inflectional change "boat -boats" is not sensepreserving.


Most verbs are also far less frequent than common negation words, making individual verbal shifters seem less important.

overall, verbal shifter lemmas occur 2.6 times as often as negation words (see §4.).


Further, the notion of shifting is most prototypically used for situations where a discrete polarity switch occurs between the classes positive, negative and neutral.

for other authors, including Polanyi and Zaenen (2006), intensification (e.g.


The most complex negation lexicon for sentiment analysis (Wilson et al., 2005) includes a mere 12 verbal shifters.

our resource covers over 1200 verbal shifter lemmas.


A verbal shifter usually only affects the parts of a sentence that are syntactically governed by the verb through its valency.

not every argument of a verbal shifter is subject to polarity shifting.


A common approach to understanding complex controversial issues is to hire experts and conduct surveys.

such an approach has inherent limitations: the survey creators inadvertently bring in biases, often these surveys fail to cover all relevant aspects, and the process is time intensive and expensive.


These new natural language processing tasks are a way to summarize information about the controversial issues without necessarily having to do the crowdsourcing described above.

the crowdsourced data will serve as a source of reference (gold) labels for the evaluation of these NLP algorithms.


To calculate these scores, we reuse the formula shown in equation 2.

the percentages are now calculated only on the set of persons that have agreed to (support score) or disagreed with (oppose score) the assertion.


For the issues Climate Change, Gender Equality, Media Bias, Mandatory Vaccination, and Obama Care the scores are even below 0.5, which means that on average there is more consensus than dissent in judging the assertions on these issues.

as shown by the issues Same-sex Marriage (0.66), Marijuana (0.69) and Vegetarianism & Veganism (0.73), our data contains also more polarizing issues.


We believe that, when a newspaper has a clear political bias (left-wing or right-wing), most of its readers will share the same ideologies and they will express similar emotions in their replies.

when a newspaper has an intermediate position (centrist), the opinions expressed by its readers will be diverse as well as their emotions.


As such it has become imperative that such behaviours be recognised and dealt with using automatic or semi-automatic means.

as much as we want to deal with this automatically, it is not quite that easy to automatically recognise these, especially, using the traditional dictionary look-up or similar methods.


Additionally, all the categories were defined more rigorously, thereby, reducing the scope of different interpretations by the annotators.

given the fact that aggression is a pragmatic phenomenon, the guidelines still gave annotators the flexibility of giving judgments based on their interpretation, instead of fixing the structures, lexical items, etc for each effect.


Intrinsic evaluation tries to directly quantify how well various kinds of linguistic regularities can be detected with the model independent of its downstream applications (Baroni et al., 2014;Schnabel et al., 2015).

the quality of a word vector may be assessed by its performance in downstream tasks through measuring changes in performance metrics specific to the tasks by extrinsic evaluation.


For example, different relation categories benefit from different context windows size in different ways, such as the model with larger context windows   tends to capture antonymy relation while with smaller windows, learns synonymy relation of the words.

negative sampling and frequency cut-off parameters have different impacts in the three relation categories.


It shows a large improvement in all evaluations when the dimensionality is increased.

the improvement peaks at 400 for the synonymy and antonymy predictions and 500 for alternative form.


For example, the statistical methods extracted related terms such as m`lh 4 (degree) and clsiws (Celsius) for the target term TmprTwrh ((physics) temperature).

these related terms do not appear in Hebrew WordNet and thus would be judged irrelevant.


Due to the problematic gold-standard (Hebrew Wordnet), we do not have a decisive conclusion on the best configuration for term representation.

we did demonstrate the importance of the methodological scheme by showing that the default configuration is definitely not the optimal one.


(2009), linguistic features were not taken into account for the selection of the samples.

to the corpus of the Baker study that consisted of speech based on a pre-written dialogue, the corpora for this project contained mostly free speech.


As a consequence of not controlling the linguistic features within the single samples, the difficulty of locating a specific sample might also have differed depending on the amount of salient features of a dialect that appeared in a sample, especially in the case of lexical or certain phonological cues.

as all the samples are longer than 10 seconds, it is very unlikely that no salient features would be included in any of the samples.


Conversational communication between bilingual speakers represents the dynamic nature of code-mixing in nearly all its entirety.

there are large sections of print media now that employ recurrent patterns of code-mixing, if not switching.


A pivot language, which is usually English, can bridge the source and target languages and make translation possible.

the domains of these two are often different and thus results in low performance and even ambiguities.


As shown in Figure 2, we develop an end-to-end system to automatically build our parallel corpus from bilingual websites.

there are still some challenges: 1) how to identify parallel/comparable news articles (bilingual document alignment tasks); 2) bilingual news articles are not direct translations to each other but written separately by Chinese authors and Portuguese authors of the same story.


As shown in Table 1, vocabulary size is very big on both corpora.

nMT models typically operate with a fixed vocabulary, which results in the OOV problem.


Standard image search engines are limited to pictures that already exist in their databases, biasing them toward retrieving images of mundane and real-world scenarios.

a scene generation system like WordsEye can illustrate a much wider range of images, allowing users to visualize unusual and fantastical scenes.


The annotation consists in enriching the documents with summaries, keywords or participant names in order to satisfy the complex queries elaborated by INA customers or researchers within media databases.

due to the increasing number of documents and the limited number of annotators, many documents remain undocumented or only partly documented.


The sign test indicates that there is a statistically significant difference between these models (p = 5.7e −202 ).

the performance of the TE label prediction model trained and tested on the SICK corpus is close to the performance of the baseline model (56.7%).


In (Zuanović et al., 2014) authors translated small portion from English analogy dataset to Croatian in order to evaluate their Neural based model.

this translation of syntactic analogy reasoning dataset was only made for a total of 350 questions based on positive-comparative form relationship in adjectives.


Some approaches started to enrich the text representation by exploiting its semantic meaning by using the Latent Semantic Analysis (LSA) (Choi et al., 2001).

these approaches require a very large corpus, and consequently the pre-processing effort required is significant.


This is because at each stage in the algorithm the proximity of the newly merged object to all other available segments is computed.

in C-HTS, we apply the hierarchical agglomerative clustering on text level.


We argue that this is because in the Moonstone dataset the boundary for each level, in each document, was placed by a number of different annotators, hence, there can be mixed agreement between those annotators on the correct placement of the level boundary.

in Wikipedia dataset, the original article hierarchy (where levels are obtained from) was created and updated with the agreement of the Wikipedia article contributors.


In HAPS, the desired number of levels needs to be passed as a parameter to the algorithm.

in C-HTS, it does not need to know number of levels that are needed in the output structure because the structure produced by C-HTS depends on the coherence between the atomic units of the text.


Automatically scoring metaphor novelty is an unexplored topic in natural language processing, and research in this area could benefit a wide range of NLP tasks.

no publicly available metaphor novelty datasets currently exist, making it difficult to perform research on this topic.


Systems capable of distinguishing between different grades of metaphor novelty could thus learn ways to assess cognitive health based on a user's per-ceived comprehension of different metaphors.

data scarcity currently acts as a barrier to research activity in these promising application areas.


The idea to use distributional semantics to find hypernyms seems natural and has been widely used.

the existing methods used distributional, yet sense-unaware and local features.


words "apple" and "mango" have distinct "fruit" senses, represented by a list of related senses.

sense clusters represent a global and not a local clustering of senses, i.e.


Similarly to the induced word senses, the semantic classes are labeled with hypernyms.

to the induced word senses, which represent a local clustering of word senses (related to a given word) semantic classes represent a global sense clustering of word senses.


Distributional representations of rare words, such as "mangosteen" can be less precise than those of frequent words.

co-occurrence of a hyponym and a hypernym in a single sentence is not required in our approach, while it is the case for the path-based hypernymy extraction methods.


Secondly, for the unpruned model (t = 0), edge weights based on counts worked better than logarithmic weights.

when pruned (t > 0), logarithmic edge weighting shows better results.


The improvements of recall are due to the fact that to label a cluster of co-hyponyms it is sufficient to lookup hypernyms for only a fraction of words in the clusters.

binary relations will be generated between all cluster hypernyms and the cluster words potentially generating hypernyms missing in the input database.


(2008) recognize 23% of relation mentions in a biomedical dataset as inter-sentence relation instances.

a major bottleneck for investigating inter-sentence relation extraction is the absence of a significantly large dataset with inter-sentence relation mentions.


(2017) have investigated inter-sentence relation extraction on a large dataset.

the study is focused on a specialised domain such drug-gene interaction.


For instance, as seen in Listing 3, the sample sentence pair for the relation business/company/industry does not provide an explicitly visible relationship between the entities for the said relation.

the seed instances used identifies "Google" as a "Search" industry, resulting in obtaining this sentence pair as a suitable candidate for inter-sentence relation extraction.


These results shows that these models are able to easily learn from the available features (words) for these relations.

as seen in Table 4, there are a number of relations, where the models achieve a significantly lower F-score.


(2015b) report an F-score of 0.82 by training an LSTM model using word embeddings.

instead of using all the words between the entities in the sentence, Xu et al.


Naturally, users type without explicit accents and rely on the auto-completion systems.

these systems are usually simple, unigrambased, and based on the word form ambiguity for a given language (cf.


Also, first-order syntactic features (extracted directly from the dependency relations themselves) and secondorder syntactic features (the partial analysis of a word, when already linked in the partial tree) complete the linguistic information usually exploited for automatic parsing.

additional features, whenever available, are easy to integrate in data-driven systems and our initial intuition was that semantic features, like wordnet relations, sub-categorization frames and semantic classes can increase parsing performance.


This classifier beats the majority baseline overall and performs relatively well in most areas.

it has a very low recall when identifying None type sluices.


Among all the multi-modal resources for emotion detection, textual datasets are those containing the least additional information in addition to semantics, and hence are adopted widely for testing the developed systems.

most of the textual emotional datasets consist of emotion labels of only individual words, sentences or documents, which makes it challenging to discuss the contextual flow of emotions.


The IEMOCAP database (Busso et al., 2008), to the best of our knowledge, is the only dataset that provides emotion labels for each utterance.

iEMOCAP was created by actors performing emotions, and hence carries the risk of overacting.


In academic research we are often focused on a highly specific problem, and we can make extensive assumptions about aspects that are not in the immediate center of attention.

industrial endeavors require a more holistic view; they need to work with given practical settings and address specific requirements of live systems.


Relatively recent orthographic reforms have made the modern Javanese and Sundanese orthographies highly regular and phonemically transparent.

in conventional usage several ambiguities arise.


(1987), warrant is indistinguishable from data.

argument schemes capture specific patterns of argument that are in use; each argument scheme specifies specific premises for the given conclusion, as well as critical questions that can be used to examine the strength of the given argument (Walton, 1996;Blair, 2001).


The annotator that has chosen DEFINI-TIONAL has most likely interpreted the premise as a categorization.

the categorization process is not at the basis of the inference that allows to support the conclusion.


A sad highlight: Losing 60:87 to the weakish Mannheimers in a sold-out stadium.").

due to the scarcity of data, it is not possible to statistically evaluate the significance of this preferential association.


Discourse connective prediction is related to the NLI problem since entailment and contradiction can be explicitly indicated by certain connectives (for instance, therefore and by contrast, respectively).

the larger number of classes makes connective prediction more challenging.


In a study of whether phonological inventories have become more or less complex over time, Marsico (1999) shows that languages dating back as far as 10,000 years are equally-complex in terms of their number of segments, consonant/vowel ratio, average number of consonants and vowels, and frequency hierarchy of the segments.

marsico (1999) also notes that modern languages tend to have slightly more consonants today than their ancestors did in the past.


For example, a vowel space can be expanded straightforwardly by the contrastive features length and nasalization.

length and labialization, palatalization, and velarization, can expand consonant inventories.


In addition, especially for low-resource languages, names can be an invaluable source of information for learning morphemes and their semantics.

finding their optimal translation is a challenging task, due to various reasons, including low occurrence counts and because certain names have high translation entropy or are translated into their localized proper names.


For example, the English name David is best aligned to the lemma form Depito in the Ankave language.

david is also aligned to words of the form depito + some affixes.


The digitisation efforts have made the Sanskrit manuscripts easily available in the public domain.

the accessibility of such digitised manuscripts is still limited.


when a document about a hurricane says that "housing across the island was destroyed", annotators should label a shelter need).

inference is a slippery slope, and too much use of inference can lead annotators to create frames for all possible needs typically associated with an incident -even when they are not implied by the document (e.g.